Overview

Dataset statistics

Number of variables17
Number of observations134201
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.4 MiB
Average record size in memory136.0 B

Variable types

Text1
Categorical3
Numeric13

Alerts

feature4 is highly overall correlated with feature6 and 1 other fieldsHigh correlation
feature5 is highly overall correlated with feature8High correlation
feature6 is highly overall correlated with feature4 and 1 other fieldsHigh correlation
feature8 is highly overall correlated with feature5High correlation
feature9 is highly overall correlated with feature4 and 1 other fieldsHigh correlation
feature15 is highly overall correlated with labelHigh correlation
feature2 is highly overall correlated with feature13High correlation
feature13 is highly overall correlated with feature2High correlation
label is highly overall correlated with feature15High correlation
label is highly imbalanced (66.0%)Imbalance
feature5 has unique valuesUnique
feature6 has unique valuesUnique
feature10 has unique valuesUnique
feature14 has unique valuesUnique
feature15 has unique valuesUnique
feature16 has unique valuesUnique
feature11 has 4866 (3.6%) zerosZeros
feature12 has 13139 (9.8%) zerosZeros

Reproduction

Analysis started2023-06-18 17:39:44.776172
Analysis finished2023-06-18 17:40:19.994398
Duration35.22 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Distinct481
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2023-06-18T17:40:20.211570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length59
Median length37
Mean length21.749599
Min length3

Characters and Unicode

Total characters2918818
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSite engineer
2nd rowSite engineer
3rd rowSite engineer
4th rowSite engineer
5th rowSite engineer
ValueCountFrequency (%)
officer 13339
 
4.1%
engineer 13231
 
4.1%
manager 9818
 
3.1%
and 6354
 
2.0%
scientist 5433
 
1.7%
surveyor 5413
 
1.7%
civil 3865
 
1.2%
health 3858
 
1.2%
therapist 3854
 
1.2%
psychologist 3796
 
1.2%
Other values (450) 252639
78.6%
2023-06-18T17:40:20.694659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 300566
 
10.3%
i 255305
 
8.7%
r 236654
 
8.1%
a 208071
 
7.1%
n 203662
 
7.0%
t 198736
 
6.8%
187399
 
6.4%
o 170179
 
5.8%
s 151529
 
5.2%
c 140807
 
4.8%
Other values (43) 865910
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2526944
86.6%
Space Separator 187399
 
6.4%
Uppercase Letter 141788
 
4.9%
Other Punctuation 57355
 
2.0%
Open Punctuation 2666
 
0.1%
Close Punctuation 2666
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 300566
11.9%
i 255305
10.1%
r 236654
9.4%
a 208071
 
8.2%
n 203662
 
8.1%
t 198736
 
7.9%
o 170179
 
6.7%
s 151529
 
6.0%
c 140807
 
5.6%
l 115857
 
4.6%
Other values (16) 545578
21.6%
Uppercase Letter
ValueCountFrequency (%)
C 15300
10.8%
E 14996
10.6%
S 14717
10.4%
P 13278
9.4%
T 12323
 
8.7%
A 11292
 
8.0%
H 7456
 
5.3%
M 7092
 
5.0%
R 6444
 
4.5%
I 6009
 
4.2%
Other values (11) 32881
23.2%
Other Punctuation
ValueCountFrequency (%)
, 40884
71.3%
/ 15561
 
27.1%
' 910
 
1.6%
Space Separator
ValueCountFrequency (%)
187399
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2666
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2666
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2668732
91.4%
Common 250086
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 300566
11.3%
i 255305
 
9.6%
r 236654
 
8.9%
a 208071
 
7.8%
n 203662
 
7.6%
t 198736
 
7.4%
o 170179
 
6.4%
s 151529
 
5.7%
c 140807
 
5.3%
l 115857
 
4.3%
Other values (37) 687366
25.8%
Common
ValueCountFrequency (%)
187399
74.9%
, 40884
 
16.3%
/ 15561
 
6.2%
( 2666
 
1.1%
) 2666
 
1.1%
' 910
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2918818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 300566
 
10.3%
i 255305
 
8.7%
r 236654
 
8.1%
a 208071
 
7.1%
n 203662
 
7.0%
t 198736
 
6.8%
187399
 
6.4%
o 170179
 
5.8%
s 151529
 
5.2%
c 140807
 
4.8%
Other values (43) 865910
29.7%

feature2
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
grocery_pos
14217 
shopping_pos
13133 
home
12471 
kids_pets
11295 
gas_transport
10898 
Other values (9)
72187 

Length

Max length14
Median length12
Mean length10.47025
Min length4

Characters and Unicode

Total characters1405118
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgrocery_pos
2nd rowgas_transport
3rd rowgrocery_pos
4th rowshopping_net
5th rowhealth_fitness

Common Values

ValueCountFrequency (%)
grocery_pos 14217
10.6%
shopping_pos 13133
9.8%
home 12471
9.3%
kids_pets 11295
8.4%
gas_transport 10898
8.1%
shopping_net 10879
8.1%
food_dining 9633
 
7.2%
personal_care 9526
 
7.1%
entertainment 9171
 
6.8%
misc_pos 8730
 
6.5%
Other values (4) 24248
18.1%

Length

2023-06-18T17:40:20.867974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
grocery_pos 14217
10.6%
shopping_pos 13133
9.8%
home 12471
9.3%
kids_pets 11295
8.4%
gas_transport 10898
8.1%
shopping_net 10879
8.1%
food_dining 9633
 
7.2%
personal_care 9526
 
7.1%
entertainment 9171
 
6.8%
misc_pos 8730
 
6.5%
Other values (4) 24248
18.1%

Most occurring characters

ValueCountFrequency (%)
s 144885
10.3%
e 132966
9.5%
o 132026
9.4%
n 122066
8.7%
p 115823
 
8.2%
_ 108283
 
7.7%
t 103466
 
7.4%
r 93841
 
6.7%
i 86890
 
6.2%
g 64316
 
4.6%
Other values (10) 300556
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1296835
92.3%
Connector Punctuation 108283
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 144885
11.2%
e 132966
10.3%
o 132026
10.2%
n 122066
9.4%
p 115823
8.9%
t 103466
8.0%
r 93841
 
7.2%
i 86890
 
6.7%
g 64316
 
5.0%
a 62030
 
4.8%
Other values (9) 238526
18.4%
Connector Punctuation
ValueCountFrequency (%)
_ 108283
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1296835
92.3%
Common 108283
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 144885
11.2%
e 132966
10.3%
o 132026
10.2%
n 122066
9.4%
p 115823
8.9%
t 103466
8.0%
r 93841
 
7.2%
i 86890
 
6.7%
g 64316
 
5.0%
a 62030
 
4.8%
Other values (9) 238526
18.4%
Common
ValueCountFrequency (%)
_ 108283
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1405118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 144885
10.3%
e 132966
9.5%
o 132026
9.4%
n 122066
8.7%
p 115823
 
8.2%
_ 108283
 
7.7%
t 103466
 
7.4%
r 93841
 
6.7%
i 86890
 
6.2%
g 64316
 
4.6%
Other values (10) 300556
21.4%

feature3
Real number (ℝ)

Distinct28125
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.995174
Minimum1
Maximum15861.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-06-18T17:40:21.047293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q19.9
median48.59
Q390.84
95-th percentile341.82
Maximum15861.4
Range15860.4
Interquartile range (IQR)80.94

Descriptive statistics

Standard deviation205.88166
Coefficient of variation (CV)2.0797141
Kurtosis536.85962
Mean98.995174
Median Absolute Deviation (MAD)39.15
Skewness12.158061
Sum13285251
Variance42387.259
MonotonicityNot monotonic
2023-06-18T17:40:21.240705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.11 69
 
0.1%
1.92 64
 
< 0.1%
1.15 64
 
< 0.1%
3.06 64
 
< 0.1%
1.41 61
 
< 0.1%
1.14 61
 
< 0.1%
3.54 61
 
< 0.1%
3.17 59
 
< 0.1%
1.54 59
 
< 0.1%
3.95 58
 
< 0.1%
Other values (28115) 133581
99.5%
ValueCountFrequency (%)
1 38
< 0.1%
1.01 44
< 0.1%
1.02 39
< 0.1%
1.03 39
< 0.1%
1.04 52
< 0.1%
1.05 48
< 0.1%
1.06 43
< 0.1%
1.07 52
< 0.1%
1.08 54
< 0.1%
1.09 54
< 0.1%
ValueCountFrequency (%)
15861.4 1
< 0.1%
13708 1
< 0.1%
9875.47 1
< 0.1%
9186.99 1
< 0.1%
8192.07 1
< 0.1%
7559.34 1
< 0.1%
6002.32 1
< 0.1%
5252.81 1
< 0.1%
4841.43 1
< 0.1%
4798.5 1
< 0.1%

feature4
Real number (ℝ)

Distinct893
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50721.259
Minimum1106
Maximum99791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-06-18T17:40:21.450958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1106
5-th percentile7302
Q128152
median46222
Q378045
95-th percentile94954
Maximum99791
Range98685
Interquartile range (IQR)49893

Descriptive statistics

Standard deviation29578.182
Coefficient of variation (CV)0.58315158
Kurtosis-1.3174572
Mean50721.259
Median Absolute Deviation (MAD)26887
Skewness0.07033489
Sum6.8068437 × 109
Variance8.7486887 × 108
MonotonicityNot monotonic
2023-06-18T17:40:21.644320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85051 659
 
0.5%
44053 593
 
0.4%
94509 590
 
0.4%
93458 473
 
0.4%
22193 467
 
0.3%
7650 367
 
0.3%
32669 366
 
0.3%
48095 363
 
0.3%
28152 361
 
0.3%
56224 361
 
0.3%
Other values (883) 129601
96.6%
ValueCountFrequency (%)
1106 238
0.2%
1431 178
0.1%
1550 286
0.2%
1570 354
0.3%
1609 179
0.1%
1701 184
0.1%
1760 178
0.1%
1880 177
0.1%
1902 180
0.1%
2081 125
 
0.1%
ValueCountFrequency (%)
99791 233
0.2%
99737 234
0.2%
99709 7
 
< 0.1%
99654 178
0.1%
99508 12
 
< 0.1%
99218 181
0.1%
99206 181
0.1%
98354 299
0.2%
98312 118
 
0.1%
98230 297
0.2%

feature5
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct134201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.236698
Minimum11.873034
Maximum76.845878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-06-18T17:40:21.838774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11.873034
5-th percentile27.808553
Q133.367728
median37.492299
Q341.133355
95-th percentile45.48065
Maximum76.845878
Range64.972844
Interquartile range (IQR)7.7656272

Descriptive statistics

Standard deviation5.7195164
Coefficient of variation (CV)0.15359892
Kurtosis2.4208133
Mean37.236698
Median Absolute Deviation (MAD)3.8585081
Skewness0.38352382
Sum4997202.1
Variance32.712868
MonotonicityNot monotonic
2023-06-18T17:40:22.028818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.21343879 1
 
< 0.1%
38.48606569 1
 
< 0.1%
37.12118263 1
 
< 0.1%
37.33541726 1
 
< 0.1%
36.81711117 1
 
< 0.1%
40.8536771 1
 
< 0.1%
37.53080221 1
 
< 0.1%
35.66482872 1
 
< 0.1%
37.25700491 1
 
< 0.1%
37.47805552 1
 
< 0.1%
Other values (134191) 134191
> 99.9%
ValueCountFrequency (%)
11.87303371 1
< 0.1%
14.89140096 1
< 0.1%
14.95912265 1
< 0.1%
15.07966291 1
< 0.1%
15.11185023 1
< 0.1%
15.67944168 1
< 0.1%
16.01816068 1
< 0.1%
16.02176482 1
< 0.1%
16.16453759 1
< 0.1%
16.40507518 1
< 0.1%
ValueCountFrequency (%)
76.8458781 1
< 0.1%
75.47804188 1
< 0.1%
74.71848258 1
< 0.1%
74.59075688 1
< 0.1%
74.3803469 1
< 0.1%
74.17610596 1
< 0.1%
74.08888983 1
< 0.1%
74.06084688 1
< 0.1%
73.95149566 1
< 0.1%
73.80880658 1
< 0.1%

feature6
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct134201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.838641
Minimum-173.21991
Maximum-63.066068
Zeros0
Zeros (%)0.0%
Negative134201
Negative (%)100.0%
Memory size1.0 MiB
2023-06-18T17:40:22.237753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-173.21991
5-th percentile-121.2964
Q1-98.892598
median-87.142421
Q3-79.599335
95-th percentile-72.406641
Maximum-63.066068
Range110.15384
Interquartile range (IQR)19.293263

Descriptive statistics

Standard deviation16.339139
Coefficient of variation (CV)-0.17791138
Kurtosis0.55543038
Mean-91.838641
Median Absolute Deviation (MAD)9.378661
Skewness-0.95982136
Sum-12324838
Variance266.96747
MonotonicityNot monotonic
2023-06-18T17:40:22.427012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-85.2037563 1
 
< 0.1%
-123.2611282 1
 
< 0.1%
-121.7739182 1
 
< 0.1%
-122.494415 1
 
< 0.1%
-120.0465399 1
 
< 0.1%
-117.9350593 1
 
< 0.1%
-118.0754231 1
 
< 0.1%
-120.8780758 1
 
< 0.1%
-121.4645813 1
 
< 0.1%
-119.7140041 1
 
< 0.1%
Other values (134191) 134191
> 99.9%
ValueCountFrequency (%)
-173.2199057 1
< 0.1%
-172.6492897 1
< 0.1%
-172.0358687 1
< 0.1%
-172.0274177 1
< 0.1%
-171.9630915 1
< 0.1%
-171.1974443 1
< 0.1%
-171.0406383 1
< 0.1%
-170.6418147 1
< 0.1%
-170.5758417 1
< 0.1%
-170.5605941 1
< 0.1%
ValueCountFrequency (%)
-63.06606843 1
< 0.1%
-63.42690452 1
< 0.1%
-64.92263618 1
< 0.1%
-65.04017887 1
< 0.1%
-65.1725452 1
< 0.1%
-65.34259827 1
< 0.1%
-65.35198726 1
< 0.1%
-65.35629868 1
< 0.1%
-65.37930797 1
< 0.1%
-65.41633269 1
< 0.1%

feature7
Real number (ℝ)

Distinct739
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293853.95
Minimum194
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-06-18T17:40:22.806598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum194
5-th percentile2661
Q116719
median62009
Q3247530
95-th percentile1577385
Maximum2906700
Range2906506
Interquartile range (IQR)230811

Descriptive statistics

Standard deviation552713.29
Coefficient of variation (CV)1.8809115
Kurtosis8.524005
Mean293853.95
Median Absolute Deviation (MAD)54143
Skewness2.8610575
Sum3.9435494 × 1010
Variance3.0549198 × 1011
MonotonicityNot monotonic
2023-06-18T17:40:22.996398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2906700 1618
 
1.2%
1595797 1345
 
1.0%
1577385 1141
 
0.9%
1382480 1138
 
0.8%
1312922 1092
 
0.8%
2383912 1052
 
0.8%
2504700 973
 
0.7%
790689 917
 
0.7%
910148 902
 
0.7%
67952 882
 
0.7%
Other values (729) 123141
91.8%
ValueCountFrequency (%)
194 123
 
0.1%
237 233
0.2%
333 361
0.3%
392 71
 
0.1%
441 186
0.1%
456 184
0.1%
614 68
 
0.1%
631 74
 
0.1%
710 231
0.2%
769 62
 
< 0.1%
ValueCountFrequency (%)
2906700 1618
1.2%
2680484 314
 
0.2%
2504700 973
0.7%
2383912 1052
0.8%
1737737 476
 
0.4%
1595797 1345
1.0%
1577385 1141
0.9%
1526206 434
 
0.3%
1417793 366
 
0.3%
1382480 1138
0.8%

feature8
Real number (ℝ)

Distinct133688
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.234688
Minimum18.798261
Maximum71.485302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-06-18T17:40:23.195077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18.798261
5-th percentile28.264083
Q133.602904
median37.544626
Q340.976075
95-th percentile44.639542
Maximum71.485302
Range52.687041
Interquartile range (IQR)7.373171

Descriptive statistics

Standard deviation5.3845784
Coefficient of variation (CV)0.14461188
Kurtosis3.1425973
Mean37.234688
Median Absolute Deviation (MAD)3.662394
Skewness0.47988769
Sum4996932.4
Variance28.993685
MonotonicityNot monotonic
2023-06-18T17:40:23.396424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.676577 2
 
< 0.1%
42.226678 2
 
< 0.1%
42.220395 2
 
< 0.1%
40.282775 2
 
< 0.1%
43.874085 2
 
< 0.1%
40.697807 2
 
< 0.1%
33.335547 2
 
< 0.1%
39.954891 2
 
< 0.1%
42.338957 2
 
< 0.1%
41.937471 2
 
< 0.1%
Other values (133678) 134181
> 99.9%
ValueCountFrequency (%)
18.798261 1
< 0.1%
18.823393 1
< 0.1%
18.829194 1
< 0.1%
18.83863 1
< 0.1%
18.850059 1
< 0.1%
18.862375 1
< 0.1%
18.883823 1
< 0.1%
18.894883 1
< 0.1%
18.909904 1
< 0.1%
18.912901 1
< 0.1%
ValueCountFrequency (%)
71.485302 1
< 0.1%
71.482581 1
< 0.1%
71.468627 1
< 0.1%
71.457397 1
< 0.1%
71.449817 1
< 0.1%
71.425062 1
< 0.1%
71.415706 1
< 0.1%
71.414967 1
< 0.1%
71.409252 1
< 0.1%
71.392575 1
< 0.1%

feature9
Real number (ℝ)

Distinct133967
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.844922
Minimum-169.01967
Maximum-69.13386
Zeros0
Zeros (%)0.0%
Negative134201
Negative (%)100.0%
Memory size1.0 MiB
2023-06-18T17:40:23.596310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-169.01967
5-th percentile-121.44173
Q1-98.069477
median-86.945641
Q3-80.010288
95-th percentile-73.13566
Maximum-69.13386
Range99.885809
Interquartile range (IQR)18.059189

Descriptive statistics

Standard deviation16.224433
Coefficient of variation (CV)-0.1766503
Kurtosis0.58171777
Mean-91.844922
Median Absolute Deviation (MAD)9.399738
Skewness-0.98164646
Sum-12325680
Variance263.23222
MonotonicityNot monotonic
2023-06-18T17:40:23.785333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-82.650219 2
 
< 0.1%
-86.99192 2
 
< 0.1%
-81.807377 2
 
< 0.1%
-74.344951 2
 
< 0.1%
-85.165589 2
 
< 0.1%
-81.049725 2
 
< 0.1%
-99.115179 2
 
< 0.1%
-82.307623 2
 
< 0.1%
-122.988165 2
 
< 0.1%
-73.905525 2
 
< 0.1%
Other values (133957) 134181
> 99.9%
ValueCountFrequency (%)
-169.019669 1
< 0.1%
-169.019569 1
< 0.1%
-168.961024 1
< 0.1%
-168.959559 1
< 0.1%
-168.956874 1
< 0.1%
-168.949138 1
< 0.1%
-168.93595 1
< 0.1%
-168.898636 1
< 0.1%
-168.879543 1
< 0.1%
-168.873607 1
< 0.1%
ValueCountFrequency (%)
-69.13386 1
< 0.1%
-69.134307 1
< 0.1%
-69.137203 1
< 0.1%
-69.137284 1
< 0.1%
-69.141322 1
< 0.1%
-69.15635 1
< 0.1%
-69.157873 1
< 0.1%
-69.160535 1
< 0.1%
-69.170033 1
< 0.1%
-69.173353 1
< 0.1%

feature10
Real number (ℝ)

Distinct134201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.272938
Minimum23.447657
Maximum97.121303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-06-18T17:40:23.980172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum23.447657
5-th percentile26.996285
Q134.949863
median44.951903
Q358.63852
95-th percentile81.262134
Maximum97.121303
Range73.673646
Interquartile range (IQR)23.688658

Descriptive statistics

Standard deviation16.670031
Coefficient of variation (CV)0.3453287
Kurtosis-0.098020166
Mean48.272938
Median Absolute Deviation (MAD)11.478462
Skewness0.77448827
Sum6478276.5
Variance277.88993
MonotonicityNot monotonic
2023-06-18T17:40:24.178978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.59606218 1
 
< 0.1%
40.9696196 1
 
< 0.1%
39.88999032 1
 
< 0.1%
41.09778834 1
 
< 0.1%
41.25147157 1
 
< 0.1%
41.93139608 1
 
< 0.1%
40.4150176 1
 
< 0.1%
40.43900379 1
 
< 0.1%
40.38357223 1
 
< 0.1%
40.69024 1
 
< 0.1%
Other values (134191) 134191
> 99.9%
ValueCountFrequency (%)
23.44765672 1
< 0.1%
23.76904238 1
< 0.1%
23.86962919 1
< 0.1%
23.91537752 1
< 0.1%
23.94502762 1
< 0.1%
23.96328287 1
< 0.1%
23.99754265 1
< 0.1%
24.02708902 1
< 0.1%
24.03170502 1
< 0.1%
24.07089988 1
< 0.1%
ValueCountFrequency (%)
97.12130289 1
< 0.1%
96.99612844 1
< 0.1%
96.89987523 1
< 0.1%
96.79043027 1
< 0.1%
96.78070484 1
< 0.1%
96.71757679 1
< 0.1%
96.70224064 1
< 0.1%
96.68846318 1
< 0.1%
96.68206388 1
< 0.1%
96.60726724 1
< 0.1%

feature11
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.727856
Minimum0
Maximum23
Zeros4866
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-06-18T17:40:24.362738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median14
Q319
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.0487407
Coefficient of variation (CV)0.55380424
Kurtosis-1.1605634
Mean12.727856
Median Absolute Deviation (MAD)6
Skewness-0.24495943
Sum1708091
Variance49.684745
MonotonicityNot monotonic
2023-06-18T17:40:24.514489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
22 8377
 
6.2%
23 8334
 
6.2%
20 6433
 
4.8%
13 6418
 
4.8%
19 6388
 
4.8%
17 6349
 
4.7%
12 6319
 
4.7%
21 6297
 
4.7%
18 6294
 
4.7%
16 6292
 
4.7%
Other values (14) 66700
49.7%
ValueCountFrequency (%)
0 4866
3.6%
1 4908
3.7%
2 5073
3.8%
3 5024
3.7%
4 4182
3.1%
5 4375
3.3%
6 4337
3.2%
7 4356
3.2%
8 4274
3.2%
9 4291
3.2%
ValueCountFrequency (%)
23 8334
6.2%
22 8377
6.2%
21 6297
4.7%
20 6433
4.8%
19 6388
4.8%
18 6294
4.7%
17 6349
4.7%
16 6292
4.7%
15 6254
4.7%
14 6247
4.7%

feature12
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4779025
Minimum0
Maximum6
Zeros13139
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-06-18T17:40:24.650897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9938854
Coefficient of variation (CV)0.57330112
Kurtosis-1.1908398
Mean3.4779025
Median Absolute Deviation (MAD)2
Skewness-0.296005
Sum466738
Variance3.9755791
MonotonicityNot monotonic
2023-06-18T17:40:24.808181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 27584
20.6%
5 24375
18.2%
4 20711
15.4%
2 17867
13.3%
1 15397
11.5%
3 15128
11.3%
0 13139
9.8%
ValueCountFrequency (%)
0 13139
9.8%
1 15397
11.5%
2 17867
13.3%
3 15128
11.3%
4 20711
15.4%
5 24375
18.2%
6 27584
20.6%
ValueCountFrequency (%)
6 27584
20.6%
5 24375
18.2%
4 20711
15.4%
3 15128
11.3%
2 17867
13.3%
1 15397
11.5%
0 13139
9.8%

feature13
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
69230 
2
62445 
3
 
2526

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134201
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 69230
51.6%
2 62445
46.5%
3 2526
 
1.9%

Length

2023-06-18T17:40:24.963403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T17:40:25.129185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 69230
51.6%
2 62445
46.5%
3 2526
 
1.9%

Most occurring characters

ValueCountFrequency (%)
1 69230
51.6%
2 62445
46.5%
3 2526
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134201
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 69230
51.6%
2 62445
46.5%
3 2526
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 134201
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 69230
51.6%
2 62445
46.5%
3 2526
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134201
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 69230
51.6%
2 62445
46.5%
3 2526
 
1.9%

label
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
125704 
1
 
8497

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134201
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 125704
93.7%
1 8497
 
6.3%

Length

2023-06-18T17:40:25.266947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T17:40:25.423902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 125704
93.7%
1 8497
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 125704
93.7%
1 8497
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134201
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125704
93.7%
1 8497
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 134201
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125704
93.7%
1 8497
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134201
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125704
93.7%
1 8497
 
6.3%

feature14
Real number (ℝ)

Distinct134201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4960607
Minimum-1.0035501
Maximum16.812962
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)< 0.1%
Memory size1.0 MiB
2023-06-18T17:40:25.574478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.0035501
5-th percentile4.1926212
Q16.1423066
median7.498909
Q38.8490464
95-th percentile10.788789
Maximum16.812962
Range17.816512
Interquartile range (IQR)2.7067398

Descriptive statistics

Standard deviation2.0030492
Coefficient of variation (CV)0.26721358
Kurtosis0.0035213268
Mean7.4960607
Median Absolute Deviation (MAD)1.3532393
Skewness-0.0080284672
Sum1005978.8
Variance4.0122061
MonotonicityNot monotonic
2023-06-18T17:40:25.758659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.017864755 1
 
< 0.1%
9.168051523 1
 
< 0.1%
7.531186299 1
 
< 0.1%
7.935373383 1
 
< 0.1%
7.987994204 1
 
< 0.1%
10.32536268 1
 
< 0.1%
7.606166055 1
 
< 0.1%
7.289974592 1
 
< 0.1%
10.15091605 1
 
< 0.1%
5.949181714 1
 
< 0.1%
Other values (134191) 134191
> 99.9%
ValueCountFrequency (%)
-1.003550085 1
< 0.1%
-0.9290672109 1
< 0.1%
-0.847267109 1
< 0.1%
-0.6487660799 1
< 0.1%
-0.6311311885 1
< 0.1%
-0.5234251099 1
< 0.1%
-0.4840739771 1
< 0.1%
-0.4366576635 1
< 0.1%
-0.4186388095 1
< 0.1%
-0.4177912709 1
< 0.1%
ValueCountFrequency (%)
16.81296174 1
< 0.1%
15.72474357 1
< 0.1%
15.71535894 1
< 0.1%
15.67344242 1
< 0.1%
15.60196316 1
< 0.1%
15.5996583 1
< 0.1%
15.49336001 1
< 0.1%
15.27992714 1
< 0.1%
15.26951288 1
< 0.1%
15.26006981 1
< 0.1%

feature15
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct134201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53177357
Minimum-0.34274793
Maximum1.7033392
Zeros0
Zeros (%)0.0%
Negative767
Negative (%)0.6%
Memory size1.0 MiB
2023-06-18T17:40:25.958292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.34274793
5-th percentile0.17623962
Q10.37577811
median0.51705945
Q30.66675556
95-th percentile0.94798904
Maximum1.7033392
Range2.0460872
Interquartile range (IQR)0.29097745

Descriptive statistics

Standard deviation0.23425001
Coefficient of variation (CV)0.44050705
Kurtosis0.77024134
Mean0.53177357
Median Absolute Deviation (MAD)0.14531795
Skewness0.50322292
Sum71364.545
Variance0.054873066
MonotonicityNot monotonic
2023-06-18T17:40:26.135902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.028822258 1
 
< 0.1%
0.921769628 1
 
< 0.1%
0.5584495287 1
 
< 0.1%
0.6743813376 1
 
< 0.1%
0.390424133 1
 
< 0.1%
0.6733890499 1
 
< 0.1%
0.672163588 1
 
< 0.1%
0.4617404011 1
 
< 0.1%
0.4919832631 1
 
< 0.1%
0.6771587034 1
 
< 0.1%
Other values (134191) 134191
> 99.9%
ValueCountFrequency (%)
-0.3427479347 1
< 0.1%
-0.3353456445 1
< 0.1%
-0.3213764401 1
< 0.1%
-0.3058587155 1
< 0.1%
-0.2967268268 1
< 0.1%
-0.2933620709 1
< 0.1%
-0.2920209356 1
< 0.1%
-0.2884168614 1
< 0.1%
-0.2832487307 1
< 0.1%
-0.2805849503 1
< 0.1%
ValueCountFrequency (%)
1.703339221 1
< 0.1%
1.666962389 1
< 0.1%
1.652025332 1
< 0.1%
1.651679277 1
< 0.1%
1.647956543 1
< 0.1%
1.647864592 1
< 0.1%
1.627449452 1
< 0.1%
1.625455378 1
< 0.1%
1.619805196 1
< 0.1%
1.615795848 1
< 0.1%

feature16
Real number (ℝ)

Distinct134201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.236701
Minimum42.803103
Maximum88.834367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-06-18T17:40:26.316272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum42.803103
5-th percentile57.489273
Q162.775925
median66.31578
Q369.783914
95-th percentile74.721526
Maximum88.834367
Range46.031265
Interquartile range (IQR)7.0079889

Descriptive statistics

Standard deviation5.2533946
Coefficient of variation (CV)0.079312444
Kurtosis0.09721404
Mean66.236701
Median Absolute Deviation (MAD)3.5018992
Skewness-0.093354893
Sum8889031.5
Variance27.598155
MonotonicityNot monotonic
2023-06-18T17:40:26.693749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.91113205 1
 
< 0.1%
67.5992062 1
 
< 0.1%
73.33554323 1
 
< 0.1%
69.1666375 1
 
< 0.1%
68.01379208 1
 
< 0.1%
67.28325164 1
 
< 0.1%
65.70653351 1
 
< 0.1%
68.98287813 1
 
< 0.1%
66.3314471 1
 
< 0.1%
70.07217808 1
 
< 0.1%
Other values (134191) 134191
> 99.9%
ValueCountFrequency (%)
42.80310253 1
< 0.1%
44.15271061 1
< 0.1%
44.21972587 1
< 0.1%
44.4247799 1
< 0.1%
44.59322547 1
< 0.1%
44.68902034 1
< 0.1%
44.69719157 1
< 0.1%
44.70530797 1
< 0.1%
44.88639665 1
< 0.1%
45.28013744 1
< 0.1%
ValueCountFrequency (%)
88.8343673 1
< 0.1%
88.59156936 1
< 0.1%
88.10419538 1
< 0.1%
87.58466842 1
< 0.1%
87.48454737 1
< 0.1%
87.43937296 1
< 0.1%
87.28445786 1
< 0.1%
86.9780624 1
< 0.1%
86.78564545 1
< 0.1%
86.53818716 1
< 0.1%

Interactions

2023-06-18T17:40:16.743104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:50.725918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:53.085754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:55.144155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:57.359949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:59.661299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:01.863974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:03.920789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:06.099666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:08.286755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:10.278630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:12.571166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:14.754583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:16.909520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:50.918454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:53.253924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:55.322109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:57.702735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:59.841053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:02.034928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:04.262774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:06.279322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:08.454756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:10.451124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:12.753429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:14.925115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:17.061657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:51.081038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:53.406139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:55.484164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:57.861924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:00.000654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:02.194341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:04.411883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:06.440402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:08.603644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:10.604380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:12.943524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:15.074033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:17.221941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:51.255765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:53.573790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:55.655201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:58.083177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:00.179081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:02.356583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:04.573862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:06.639491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:08.765779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:10.952542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:13.119982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:15.237028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:17.376493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:51.529744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:53.728528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:55.820662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:58.239130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:00.337635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:02.512636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:04.724087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:06.800901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:08.914772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:11.120032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:13.284271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:15.386040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:17.535600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:51.700905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:53.892513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:55.990527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:58.404740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:00.518783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:02.673216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:04.882337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:06.971908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:09.073988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:11.292559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:13.450789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:15.541436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:17.867764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:51.867260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:54.047147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:56.152297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:58.563070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:00.690600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:02.831698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:05.034516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:07.132809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:09.223362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:11.448990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:13.613327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:15.693144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:18.012146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:52.027302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:54.198117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:56.308696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:58.713000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:00.855941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:02.984655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:05.178674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:07.292396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:09.366661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:11.600752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:13.768386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:15.837968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:18.177997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:52.207480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:54.368458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:56.487611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:58.886406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:01.032182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:03.155857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:05.347732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:07.466536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:09.533517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:11.773750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:13.949737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:16.004977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:18.321778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:52.368720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:54.517468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:56.642332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:59.035640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:01.189475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:03.302627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:05.491884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:07.634238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:09.674608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:11.934891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:14.105290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:16.146950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:18.506054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:52.546057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:54.678041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:56.817893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:59.198691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:01.352197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:03.464591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:05.650836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:07.802462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:09.830574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:12.098076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:14.275383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:16.304832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:18.667476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:52.761175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:54.844950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:56.997545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:59.364908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:01.527600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:03.628992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:05.812634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:07.975476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:09.992673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:12.272141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:14.444164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:16.462804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:18.807572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:52.928259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:54.990925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:57.187502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:39:59.511423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:01.678962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:03.774338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:05.954127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:08.130177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:10.134046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:12.421656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:14.596588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-18T17:40:16.599920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-18T17:40:26.859898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
feature3feature4feature5feature6feature7feature8feature9feature10feature11feature12feature14feature15feature16feature2feature13label
feature31.0000.003-0.006-0.0040.002-0.005-0.0040.018-0.096-0.0160.0040.108-0.0730.0050.0140.000
feature40.0031.000-0.206-0.9640.119-0.228-0.9760.0160.001-0.002-0.001-0.0030.0030.0070.0000.020
feature5-0.006-0.2061.0000.207-0.1730.9170.2120.035-0.0070.0020.002-0.003-0.0000.0050.0030.007
feature6-0.004-0.9640.2071.000-0.0990.2300.983-0.0240.0010.0020.0020.002-0.0020.0060.0000.007
feature70.0020.119-0.173-0.0991.000-0.188-0.099-0.0130.0000.003-0.002-0.0010.0040.0070.0020.007
feature8-0.005-0.2280.9170.230-0.1881.0000.2360.036-0.0080.002-0.000-0.001-0.0010.0070.0000.012
feature9-0.004-0.9760.2120.983-0.0990.2361.000-0.0240.0010.0030.0010.002-0.0030.0060.0000.000
feature100.0180.0160.035-0.024-0.0130.036-0.0241.000-0.1560.007-0.0010.032-0.0210.0410.0060.084
feature11-0.0960.001-0.0070.0010.000-0.0080.001-0.1561.000-0.005-0.0000.020-0.0130.2660.4570.291
feature12-0.016-0.0020.0020.0020.0030.0020.0030.007-0.0051.0000.003-0.0290.0210.1550.2530.070
feature140.004-0.0010.0020.002-0.002-0.0000.001-0.001-0.0000.0031.0000.007-0.0030.0030.0070.000
feature150.108-0.003-0.0030.002-0.001-0.0010.0020.0320.020-0.0290.0071.000-0.1050.0540.0220.704
feature16-0.0730.003-0.000-0.0020.004-0.001-0.003-0.021-0.0130.021-0.003-0.1051.0000.0280.0100.366
feature20.0050.0070.0050.0060.0070.0070.0060.0410.2660.1550.0030.0540.0281.0000.8380.226
feature130.0140.0000.0030.0000.0020.0000.0000.0060.4570.2530.0070.0220.0100.8381.0000.042
label0.0000.0200.0070.0070.0070.0120.0000.0840.2910.0700.0000.7040.3660.2260.0421.000

Missing values

2023-06-18T17:40:19.074846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-18T17:40:19.587330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

feature1feature2feature3feature4feature5feature6feature7feature8feature9feature10feature11feature12feature13labelfeature14feature15feature16
0Site engineergrocery_pos8.604823040.213439-85.2037564758342.508293-83.16800465.59606235118.0178651.02882258.911132
1Site engineergas_transport316.844823044.379391-82.8597214758342.661838-81.96651064.728795651111.7685681.10621764.431017
2Site engineergrocery_pos294.894823042.950657-84.9355424758342.580470-82.40852965.43460635117.9963590.89988157.545348
3Site engineershopping_net831.084823039.372111-84.8939734758341.948688-83.91988164.990422236118.7677201.06296662.681169
4Site engineerhealth_fitness1063.844823041.227499-83.2283924758341.544743-82.12336565.316083236118.8162220.72244663.084486
5Site engineershopping_net968.454823045.687573-83.1430264758343.148631-82.29853464.868803236116.8065401.06379560.675761
6Site engineershopping_pos981.264823037.531342-78.6985644758342.777359-82.99555265.553237186118.9131520.86873464.374301
7Site engineershopping_net1007.674823041.526875-81.5032924758341.640156-82.07004264.349109136115.1530430.90890762.226104
8Site engineermisc_net975.264823044.916573-85.2665444758341.877041-82.57005263.353419225116.7536151.02176667.357675
9Site engineershopping_net1004.324823040.651219-82.0948734758341.945489-83.26816764.515283226119.3648551.05991460.399823
feature1feature2feature3feature4feature5feature6feature7feature8feature9feature10feature11feature12feature13labelfeature14feature15feature16
134191Minerals surveyorshopping_net45.063221030.274085-81.96941684741531.081801-82.26012430.655925166105.8546420.69482756.548753
134192Minerals surveyorentertainment35.463221031.621497-77.74106684741529.816261-80.75148831.377807192206.7289410.53659764.663838
134193Minerals surveyorhealth_fitness104.283221032.486831-80.22731384741530.775710-82.50033031.190823235205.0289020.45288163.044666
134194Minerals surveyorkids_pets25.123221030.882394-81.63961584741529.376789-80.89093830.733635143207.3095830.34459371.480156
134195Minerals surveyorhome179.243221030.052630-82.47066784741530.045514-82.35824931.273273192208.4911070.30013764.687930
134196Minerals surveyorhealth_fitness132.983221030.570251-80.00163884741529.725058-81.31964531.785767234206.9302410.58558261.754724
134197Minerals surveyorhealth_fitness2.193221029.150371-81.71934484741531.233001-81.78620230.800002233207.4324640.42497061.681467
134198Minerals surveyorkids_pets3.163221034.212880-80.63496384741529.874284-81.62459130.641819236207.6402350.28853865.003013
134199Minerals surveyorentertainment7.123221028.515650-80.13907384741529.502540-82.61235030.809930205204.1931060.48900566.160873
134200Minerals surveyorentertainment7.513221031.842962-80.30777184741529.506603-80.80622731.129042212203.0826340.68973467.800253